Pre-Conference Workshop for Doctoral Students



The DGPs Section “Allgemeine Psychologie” features a 1.5 day workshop on Bayesian approaches to data analysis and cognitive modeling for doctoral students at the TeaP 2017 in Dresden.

Only 25 participants can take part in the workshop. We will consider applications on a first-come-first-serve basis with a preference for doctoral students and members of the “Allgemeine Psychologie” DGPs-section until February 15. If you want to participate, please send an email with the subject TeaP2017: Pre-Conference Workshop to

Please mention your name and affiliation in your application as well as whether you are a DGPs member or not.

Dates: The workshop will start at Saturday, March 25, 2017 at 2:30pm and end at March 26, 2017 at 4:00pm.

Location: Technische Universität Dresden, Andreas-Schubert-Bau, Zellescher Weg 19, 01069 Dresden, 2.OG Raum 206 a,b,c

Fees: The workshop is financially supported by the DGPs Section “Allgemeine Psychologie”. Thus participants will only have to pay 40 Euros (60 Euros for participants that are not members of the “Allgemeine Psychologie” DGPs-section) for participation. Tea, coffee, and soft-drinks, as well as a brown-bag lunch on Sunday are covered by this fee.

In case you are accepted, please transfer the applicable participant fee to until March 01.

Account holder: DGPs
DE43 400 501 500 034 412 072 (Sparkasse Münsterland Ost)
Purpose for transfer: TeaPworkshop [your last name]


Details: Bayesian approaches to data analysis and cognitive modeling

By Thorsten Pachur (Max Planck Institute for Human Development) & Benjamin Scheibehenne (University of Geneva)

Recent years have seen a Bayesian paradigm shift in psychological research, which fundamentally changes the way empirical data are analyzed and modeled. In this workshop, we provide an accessible, hands-on introduction into a Bayesian approach to analyzing data. First, we describe the conceptual underpinnings of Bayesian data analysis, including why a Bayesian approach provides solutions to several shortcomings of classical frequentist methods, and give an overview of the practical steps required to implement Bayesian analyses with various available tools (e.g., packages in R, JASP). Second, we illustrate the merits of Bayesian cognitive modeling, showing how to estimate model parameters and to implement hierarchical versions of cognitive models; we also describe ways to model individual differences in behavior (e.g., using latent mixture modeling).


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